Fossil-Fuel Options for Power Sector Net-Zero Emissions with Sequestration Tax Credits

Three of the main challenges in achieving rapid decarbonization of the electric power sector in the near term are getting to net-zero while maintaining grid reliability and minimizing cost. In this policy analysis, we evaluate the performance of a variety of generation strategies using this "triple objective" including nuclear, renewables with different energy storage options, and carbon-emitting generation with carbon capture and storage (CCS) and direct air capture and storage (DACS) technologies. Given the current U.S. tax credits for carbon sequestration under Section 45Q of the Internal Revenue Code, we find that two options: (1) cofiring bioenergy in existing coal-fired assets equipped with CCS, and (2) coupling existing natural gas combined-cycle plants equipped with CCS and DACS, robustly dominate other generation strategies across many assumptions and uncertainties. As a result, capacity-expansion modelers, planners, and policymakers should consider such combinations of carbon-constrained fossil-fuel and negative emissions technologies, together with modifications of the current national incentives, when designing the pathways to a carbon-free economy.

On Ambiguity Reduction and the Role of Decision Analysis during the Pandemic

The COVID-19 pandemic has created a multitude of decision problems for a variety of fields. Questions from the seriousness and breadth of the problem to the effectiveness of proposed mitigation measures have been raised. We assert that the decision sciences have a crucial role to play here, as the questions requiring answers involve complex decision making under both uncertainty and ambiguity. The collection, processing, and analysis of data is critical in providing a useful response-especially as information of fundamental importance to such decision making (base rates and transmission rates) is lacking. We propose that scarce testing resources should be diverted away from confirmatory analysis of symptomatic people, as laboratory diagnosis appears to have little decision value in treatment choice over clinical diagnosis in patients presenting with symptoms. In contrast, the exploratory use of testing resources to reduce ambiguity in estimates of the base rate of infection appears to have significant value and great practical import for public policy purposes. As these stances may be at odds with triage practices among medical practitioners, they highlight the important role the decision analyst can play in responding to the challenges of the COVID-19 pandemic.

Shore Power for Vessels Calling at U.S. Ports: Benefits and Costs

When in port, ships burn marine diesel in on-board generators to produce electricity and are significant contributors to poor local and regional air quality. Supplying ships with grid electricity can reduce these emissions. We use two integrated assessment models to quantify the benefits of reducing the emissions of NOX, SO2, PM2.5, and CO2 that would occur if shore power were used. Using historical vessel call data, we identify combinations of vessels and berths at U.S. ports that could be switched to shore power to yield the largest gains for society. Our results indicate that, depending on the social costs of pollution assumed, an air quality benefit of $70-150 million per year could be achieved by retrofitting a quarter to two-thirds of all vessels that call at U.S. ports. Such a benefit could be produced at no net cost to society (health and environmental benefits would be balanced by the cost of ship and port retrofit) but would require many ships to be equipped to receive shore power, even if doing so would result in a private loss for the operator. Policy makers could produce a net societal gain by implementing incentives and mandates to encourage a shift toward shore power.

An integrated physical dispersion and behavioral response model for risk assessment of radiological dispersion device (RDD) events

A radiological dispersion device (RDD) or "dirty" bomb is a conventional explosive wrapped in radiological material. Terrorists may use an RDD to disperse radioactive material across a populated area, causing casualties and/or economic damage. Nearly all risk assessment models for RDDs make unrealistic assumptions about public behavior in their health assessments, including assumptions that the public would stand outside in a single location indefinitely. In this article, we describe an approach for assessing the risks of RDD events incorporating both physical dispersion and behavioral response variables. The general approach is tested using the City of Pittsburgh, Pennsylvania as a case study. Atmospheric models simulate an RDD attack and its likely fallout, while radiation exposure models assess fatal cancer risk. We model different geographical distributions of the population based on time of day. We evaluate aggregate health impacts for different public responses (i.e., sheltering-in-place, evacuating). We find that current RDD models in use can be improved with the integration of behavioral components. Using the results from the model, we show how risk varies across several behavioral and physical variables. We show that the best policy to recommend to the public depends on many different variables, such as the amount of trauma at ground zero, the capability of emergency responders to get trauma victims to local hospitals quickly and efficiently, how quickly evacuations can take place in the city, and the amount of shielding available for shelterers. Using a parametric analysis, we develop behaviorally realistic risk assessments, we identify variables that can affect an optimal risk reduction policy, and we find that decision making can be improved by evaluating the tradeoff between trauma and cancer fatalities for various RDD scenarios before they occur.

Ecological risk ranking: development and evaluation of a method for improving public participation in environmental decision making

This article reports an extension of the Carnegie Mellon risk-ranking method to incorporate ecological risks and their attributes. On the basis of earlier risk-perception studies, we identified a set of 20 relevant attributes for describing health, safety, and environmental hazards in standardized risk summary sheets. In a series of three ranking sessions, 23 laypeople ranked 10 such hazards in a fictional Midwestern U.S. county using both holistic and multiattribute ranking procedures. Results were consistent with those from previous studies involving only health and safety hazards, providing additional evidence for the validity of the method and the replicability of the resulting rankings. Holistic and multiattribute risk rankings were reasonably consistent both for individuals and for groups. Participants reported that they were satisfied with the procedures and results, and indicated their support for using the method to advise real-world risk-management decisions. Agreement among participants increased over the course of the exercise, perhaps because the materials and deliberations helped participants to correct their misconceptions and clarify their values. Overall, health and safety attributes were judged more important than environmental attributes. However, the overlap between the importance rankings of these two sets of attributes suggests that some information about environmental impacts is important to participants' judgments in comparative risk-assessment tasks.

What number is "fifty-fifty"?: redistributing excessive 50% responses in elicited probabilities

Studies using open-ended response modes to elicit probabilistic beliefs have sometimes found an elevated frequency (or blip) at 50 in their response distributions. Our previous research suggests that this is caused by intrusion of the phrase "fifty-fifty," which represents epistemic uncertainty, rather than a true numeric probability of 50%. Such inappropriate responses pose a problem for decision analysts and others relying on probabilistic judgments. Using an explicit numeric probability scale (ranging from 0-100%) reduces thinking about uncertain events in verbal terms like "fifty-fifty," and, with it, exaggerated use of the 50 response. Here, we present two procedures for adjusting response distributions for data already collected with open-ended response modes and hence vulnerable to an exaggerated presence of 50%. Each procedure infers the prevalence of 50s had a numeric probability scale been used, then redistributes the excess. The two procedures are validated on some of our own existing data and then applied to judgments elicited from experts in groundwater pollution and bioremediation.

A deliberative method for ranking risks (I): Overview and test bed development

Risk ranking offers a potentially powerful means for gathering public input to help set risk-management priorities. In most rankings conducted to date, the categories and attributes used to describe the risks have varied widely, the materials and procedures have not been designed to facilitate comparisons among risks on all important attributes, and the validity and reproducibility of the resulting rankings have not been assessed. To address these needs, a risk-ranking method was developed in which risk experts define and categorize the risks to be ranked, identify the relevant risk attributes, and characterize the risks in a set of standardized risk summary sheets, which are then used by lay or other groups in structured ranking exercises. To evaluate this method, a test bed involving 22 health and safety risks in a fictitious middle school was created. This article provides an overview of the risk-ranking method and describes the challenges faced in designing the middle school test bed. A companion article in this issue reports on the validity of the ranking procedures and the level of agreement among risk managers regarding ranking of risks and attributes.

A deliberative method for ranking risks (II): Evaluation of validity and agreement among risk managers

A deliberative method for ranking risks was evaluated in a study involving 218 risk managers. Both holistic and multiattribute procedures were used to assess individual and group rankings of health and safety risks facing students at a fictitious middle school. Consistency between the rankings that emerged from these two procedures was reasonably high for individuals and for groups, suggesting that these procedures capture an underlying construct of riskiness. Participants reported high levels of satisfaction with their groups' decision-making processes and the resulting rankings, and these reports were corroborated by regression analyses. Risk rankings were similar across individuals and groups, even though individuals and groups did not always agree on the relative importance of risk attributes. Lower consistency between the risk rankings from the holistic and multiattribute procedures and lower agreement among individuals and groups regarding these rankings were observed for a set of high-variance risks. Nonetheless, the generally high levels of consistency, satisfaction, and agreement suggest that this deliberative method is capable of producing risk rankings that can serve as informative inputs to public risk-management decision making.

Traffic accident statistics and risk perceptions in Japan and the United States

Several recent studies have concluded that Japan and the US have different risk cultures. This study examines the actual risk environments faced by citizens in the two countries, in the domain of traffic safety, as a possible source of differences in risk perceptions. The study contrasts traffic-accident risks from several points of view (e.g. car drivers, motorcyclists, bicyclists and pedestrians) and risk statistics (e.g. death rates, relative fatality risks, and accident lethality). Results clarify the traffic risks in the two countries and confirm their potential for explaining cross-national differences in risk perceptions.

The Relationship between Historic Industrial Site Use and Environmental Contamination

A methodology is presented for estimating the probability that particular classes of environmental contaminants will be of concern at brownfield redevelopment sites. These probabilities are predicted by a logistics model that is based on qualitative information about site history and status. This qualitative information comprises data that would be collected through a Phase I Environmental Site Assessment (ESA), including historic site use, current use and ownership status, and the nature of adjacent properties. The model is fit and demonstrated using a set of 59 former industrial sites in southwestern Pennsylvania that were collected from the files of the Pennsylvania Department of Environmental Protection (PADEP). Predictive models are developed for exceedances of contaminants as grouped into the following classes: metals, chlorinated hydrocarbons, fuel hydrocarbons, and PCBs. A procedure for estimating the parametric uncertainty of the model predictions is also illustrated. This method can serve as a starting point for more effective usage of existing Phase I ESA information and for evaluation of the benefit of obtaining additional site information. By increasing the decision-making value of existing (or inexpensive) data, this method can help to reduce the information asymmetry that may be an obstacle to redevelopment.

Measuring decision sensitivity: a combined Monte Carlo-logistic regression approach

Modeling of the uncertainty of multiple input variables for a complex decision problem complicates sensitivity analysis. A method of analysis comprising stochastic simulation of the model and logistic regression of the simulated dichotomous decision variable against all of the input variables yields a direct measure of the importance of input variables to the decision. This method is demonstrated on a previously analyzed clinical decision either to continue observation or to immediately treat with anticoagulants a woman presenting with deep vein thrombosis in the first trimester of pregnancy. A relative measure of the importance of each input variable in causing a change of decision is estimated by calculating the change in the log odds attributable to variation of each input variable over its range of uncertain values compared with the total change of log odds from variation of all input variables. This method is compared with alternative measures of input variable importance, and is found to be a simple yet powerful tool for gaining quantitative insight into the nuances of a decision model.